Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab393
Keywords
machine learning; molecular representation; drug response prediction; molecular fingerprint; graph representation
Funding
- National Institutes of Health [NIH/NIGMS R35GM133346-01]
- National Science Foundation (NSF/DBI) [1452656]
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This article focuses on the application of machine learning in drug response prediction, and specifically discusses the implementation and application examples of molecular representation methods.
The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.
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